Optimized Deep Learning Approaches for Lung and Colon Cancer Classification using Histopathological Images

This research study explores the application of pre-trained Convolutional Neural Network (CNN) architectures for the classification of lung and colon cancer using histopathological images. Leveraging the LC25000 dataset, which contains 25,000 high-quality images across five categories, the research employs EfficientNetB6, ResNet34, VGG-19, MobileNetV2, and ResNet50 for image classification. Preprocessing techniques, feature extraction, and classification were carried out using CNN-based transfer learning, optimizing computational efficiency and accuracy. The results reveal that MobileNetV2 and ResNet50 outperform other models with an accuracy of 99%, followed by VGG-19 at 98% and ResNet34 at 97%. EfficientNetB6 achieved 93% accuracy, showcasing reasonable performance despite being less computationally intensive. Evaluation metrics, including precision, recall, F1 score, and accuracy, confirmed the models' efficacy in distinguishing cancerous and normal tissues. The findings demonstrate the ability of deep learning as a robust and efficient tool for early and accurate detection of lung and colon cancer, offering significant support for clinical decision-making.